报告题目 (Title)：Convergence rate of Smoothed empirical Wasserstein distance（平滑经验Wasserstein距离的收敛速度）
报告人 (Speaker)：黎怀谦 教授（天津大学）
报告时间 (Time)：2025年11月14日 (周五) 16：00-19:00
报告地点 (Place)： 腾讯会议：880-966-912
邀请人(Inviter)：阳芬芬
报告摘要：This work provides upper bounds for the expected Gaussian-smoothed p -Wasserstein distance. We analyze the convergence rate of the empirical measure Un , constructed from N samples, towards the true probability measure U on R^d . The bounds hold universally for any p>=0 —encompassing the total variation distance when p=0 —under mild moment assumptions on U . Furthermore, we extend our analysis beyond the independent and identically distributed case, deriving convergence results for dependent data generated by e -mixing sequences and Markov chains.